用可微框架训练固体用的机器学习交换关联泛函，提升精度同时保持高效。
A fully differentiable framework for training proxy Exchange Correlation Functionals for periodic systems
- 构建可微神经网络接口，让机器学习泛函无缝替代传统泛函
- 在固体系统上实现5%-10%相对误差，接近主流软件精度
- 基于PyTorch与DeepChem，便于科研人员快速实验与复现
密度泛函理论（DFT）广泛用于化学与材料科学的第一性原理模拟，但其计算成本仍是大规模体系的主要瓶颈。受机器学习交换关联（XC）泛函进展启发，本文提出一种可微框架，将机器学习模型整合进固体等周期性系统的密度泛函理论中。该框架定义了清晰的API，使神经网络模型可作为即插即用的交换关联泛函，并支持梯度贯穿完整的自洽DFT流程。框架基于Python和PyTorch实现，具备完全可微特性，可与标准深度学习工具协同使用。我们将其集成至DeepChem库，促进已有模型复用并降低实验门槛。初步基准测试显示，在GPAW与PySCF等主流电子结构软件上的相对误差为5%-10%。
原文摘要 · Abstract (English)
Density Functional Theory (DFT) is widely used for first-principles simulations in chemistry and materials science, but its computational cost remains a key limitation for large systems. Motivated by recent advances in ML-based exchange-correlation (XC) functionals, this paper introduces a differentiable framework that integrates machine learning models into density functional theory (DFT) for solids and other periodic systems. The framework defines a clean API for neural network models that can act as drop in replacements for conventional exchange-correlation (XC) functionals and enables gradients to flow through the full self-consistent DFT workflow. The framework is implemented in Python using a PyTorch backend, making it fully differentiable and easy to use with standard deep learning tools. We integrate the implementation with the DeepChem library to promote the reuse of established models and to lower the barrier for experimentation. In initial benchmarks against established electronic structure packages (GPAW and PySCF), our models achieve relative errors on the order of 5-10%.
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